Insights into cell membrane microdomain organization from live cell single particle tracking of the IgE high affinity receptor FcϵRI of mast cells

Abstract

Current models propose that the plasma membrane of animal cells is composed of heterogeneous and dynamic microdomains known variously as cytoskeletal corrals, lipid rafts and protein islands. Much of the experimental evidence for these membrane compartments is indirect. Recently, live cell single particle tracking studies using quantum dot-labeled IgE bound to its high affinity receptor FcϵRI, provided direct evidence for the confinement of receptors within micrometer-scale cytoskeletal corrals. In this study, we show that an innovative time-series analysis of single particle tracking data for the high affinity IgE receptor, FcϵRI, on mast cells provides substantial quantitative information about the submicrometer organization of the membrane. The analysis focuses on the probability distribution function of the lengths of the jumps in the positions of the quantum dots labeling individual IgE FcϵRI complexes between frames in movies of their motion. Our results demonstrate the presence, within the micrometer-scale cytoskeletal corrals, of smaller subdomains that provide an additional level of receptor confinement. There is no characteristic size for these subdomains; their size varies smoothly from a few tens of nanometers to a over a hundred nanometers. In QD-IGE labeled unstimulated cells, jumps of less than 70 nm predominate over longer jumps. Addition of multivalent antigen to crosslink the QD-IgE-FcϵRI complexes causes a rapid slowing of receptor motion followed by a long tail of mostly jumps less than 70 nm. The reduced receptor mobility likely reflects both the membrane heterogeneity revealed by the confined motion of the monomeric receptor complexes and the antigen-induced cross linking of these complexes into dimers and higher oligomers. In both cases, the probability distribution of the jump lengths is well fit, from 10 nm to over 100 nm, by a novel power law. The fit for short jumps suggests that the motion of the quantum dots can be modeled as diffusion in a fractal space of dimension less than two.

Fig. 1

IgE-FcεRI and QD-IgE-FcεRI complexes. Modified image taken from Kraft and Kinet (2007)

Fig. 2

The longest paths for the unstimulated data

Fig. 3

Time-dependent means of the jump components

Fig. 4

Time-dependent standard deviations of the jump components

Fig. 5

Distributions of the jump components and their normal fits

Fig. 6

Data angles (top) and generated random angles (bottom) for data sets A and B

Fig. 7

Jump lengths PDFs with the general chi, general Weibull and power-law fits

Fig. 8

In the top row, comparison of the simple chi and general chi distributions that have the same second moment as the data, and in the bottom row, comparison of the simple Weibull and general Weibull distributions that have the same second moment as the data

Fig. 9

Time-dependent standard deviations of the jump lengths and their exponential and power-law fits

Fig. 9

Time-dependent standard deviations of the jump lengths and their exponential and power-law fits

Fig. 10

The time- and stimulus-dependent percentages of the jump lengths

Fig. 10

The time- and stimulus-dependent percentages of the jump lengths

Fig. 11

The time-dependent standard deviations of the tails with their stationary times marked by a vertical dashed line and the mean value of s for the tails given by a horizontal solid line

Fig. 11

The time-dependent standard deviations of the tails with their stationary times marked by a vertical dashed line and the mean value of s for the tails given by a horizontal solid line

Fig. 12

Tail jump lengths PDFs with the general chi, general Weibull and power-law fits

Fig. 12

Tail jump lengths PDFs with the general chi, general Weibull and power-law fits

Fig. 13

Data set A: tracks with the longest paths

Fig. 14

Data set B: tracks with the longest paths

Fig. 15

The longest segments for data set A

Fig. 16

The longest segments for data set B

Fig. 17

The longest segments and their different jump lengths for data set A

Fig. 18

The longest segments and their different jump lengths for data set B

Fig. 19

PDFs of all the jump lengths for the data sets A and B

Fig. 20

Distributions and their normal fits of the jump components in the tails of data set A

Fig. 20

Distributions and their normal fits of the jump components in the tails of data set A

Fig. 21

Distributions and their normal fits of the jump components in the tails of data set B

Fig. 21

Distributions and their normal fits of the jump components in the tails of data set B

Fig. 22

Data angles and generated random angles in the tails of data set A

Fig. 22

Data angles and generated random angles in the tails of data set A

Fig. 23

Data angles and generated random angles in the tails of data set B

Fig. 23

Data angles and generated random angles in the tails of data set B